Cost-Benefit, Energy Sustainability and Technological Assessment of Artificial Intelligence Adoption in Nigeria’s Agricultural and Waste-to-Energy Systems

Abstract

This study examines the cost-benefit, energy sustainability, and technological implications of artificial intelligence (AI) adoption in Nigeria's agricultural and waste-to-energy (WTE) systems. AI technologies are increasingly transforming agricultural production, renewable energy generation, waste management efficiency, and environmental sustainability across developing economies. Using a quantitative survey design, data were collected from 522 respondents across Nigeria's six geopolitical zones and analysed using descriptive statistics and multiple regression techniques. The findings reveal moderate-to-high AI adoption (Mean = 3.84), significant improvements in operational efficiency (Mean = 4.02), enhanced energy recovery and environmental sustainability (Mean = 3.95), and positive social impacts (Mean = 3.78). Regression results indicate that AI investment significantly improves operational efficiency (β = 0.62, p < 0.01) and sustainability outcomes (β = 0.55, p < 0.01). The study further demonstrates that AI-enabled technologies support smart energy conversion, precision agriculture, renewable energy optimisation, and efficient waste valuation. However, infrastructural deficiencies, unstable electricity supply, limited technical expertise, and high implementation costs remain major barriers. The study concludes that AI adoption provides substantial economic, technological, and energy sustainability benefits that outweigh implementation costs. The results contribute to emerging literature on AI, renewable energy systems, and sustainable technological development in developing economies while offering practical policy recommendations for Nigeria's green transition agenda.